High-throughput　gene　expression　microarrays　can　be　examined　by　machine-learning　algorithms　to　identify　gene　signatures　that　recognize　the　biological　characteristics　of　specific　human　diseases,　including　cancer,　with　high　sensitivity　and　specificity.　A　previous　study　compared　20　gastric　cancer　(GC)　samples　against　20　normal　tissue　(NT)　samples　and　identified　1,519　differentially　expressed　genes　(DEGs).　In　this　study,　Classification　Information　Index　(CII),　Information　Gain　Index　(IGI),　and　RELIEF　algorithms　are　used　to　mine　the　previously　reported　gene　expression　profiling　data.　In　all,　29　of　these　genes　are　identified　by　all　three　algorithms　and　are　treated　as　GC　candidate　biomarkers.　Tree　biomarkers,　COL1A2,　ATP4B,　and　HADHSC,　are　selected　and　further　examined　using　quantitative　real-time　polymerase　chain　reaction　(qRT-PCR)　and　immunohistochemistry　(IHC)　staining　in　two　independent　sets　of　GC　and　normal　adjacent　tissue　(NAT)　samples.　Our　study　shows　that　COL1A2　and　HADHSC　are　the　two　best　biomarkers　from　the　microarray　data,　distinguishing　all　GC　from　the　NT,　whereas　ATP4B　is　diagnostically　significant　in　lab　tests　because　of　its　wider　range　of　fold-changes　in　expression.　Herein,　a　data-mining　model　applicable　for　small　sample　sizes　is　presented　and　discussed.　Our　result　suggested　that　this　mining　model　may　be　useful　in　small　sample-size　studies　to　identify　putative　biomarkers　and　potential　biological　features　of　GC.　©　the　authors,　publisher　and　licensee　Libertas　Academica　Limited.